Using Expert Consensus to Develop a Tool to Assess Physical Therapists’ Knowledge, Skills, and Judgement in Performing Airway Suctioning
Bibliographic record
Abstract
Purpose: The aim of this study was to develop a tool to assess physical therapists’ knowledge, skills, and judgement in performing airway suctioning with intubated and non-intubated adults. Method: A modified Delphi methodology was used to develop the tool and to evaluate its sensibility (i.e., common-sense nature). Participants were experienced cardiorespiratory physical therapists who perform airway suctioning and physical therapists employed in academic positions related to cardiorespiratory physical therapy at Canadian universities. Round 1 focused on refining which items to include in the tool, Round 2 focused on finalizing the items, and Round 3 focused on evaluating a preliminary version of the tool. Results: A total of 34 individuals participated in Round 1, 30 participated in Round 2, and 25 participated in Round 3. A literature review identified 11 relevant domains and 69 supporting competencies. In Round 1, consensus was achieved for all domains; however, it was borderline for the professionalism domain. Multiple participants suggested that it was redundant because it is a global requirement for all physical therapists. Consensus was also achieved for 64 of the 69 supporting competencies; however, it was borderline for 5 of these items, and 5 achieved no consensus. In Round 2, participants rated a series of recommendations related to items requiring further consideration, as well as 9 new items suggested by the participants in Round 1. In Round 3, the preliminary tool was found to be globally sensible, but concerns were expressed about the inclusion of redundant factors and the tool’s length. The tool was revised, resulting in a tool with 4 domains, 6 sub-domains and 43 supporting competencies, as well as an item rating the individual’s overall performance. Conclusions: The final-round sensibility questionnaire provided preliminary evidence of the tool’s face and content validity. We will investigate the tool’s measurement properties in a future study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.344 | 0.412 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".